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A probabilistic model to predict clinical phenotypic traits from genome sequencing

Chen, Yun-Ching, Douville, Christopher, Wang, Cheng, Niknafs, Noushin, Yeo, Grace, Beleva-Guthrie, Violeta, Carter, Hannah, Stenson, Peter Daniel, Cooper, David Neil, Li, Biao, Mooney, Sean and Karchin, Rachel 2014. A probabilistic model to predict clinical phenotypic traits from genome sequencing. PLoS Computational Biology 10 (9) , e1003825. 10.1371/journal.pcbi.1003825

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Abstract

Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: C Auxiliary Sciences of History > CS Genealogy
R Medicine > R Medicine (General)
Additional Information: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publisher: Public Library of Science
ISSN: 1553-7358
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 24 July 2014
Last Modified: 27 Mar 2019 14:18
URI: http://orca.cf.ac.uk/id/eprint/79104

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